Name
Abstract | ||
|---|---|---|
Patients with acetabular trauma require a treatment plan based on proper recognition and classification of acetabular fractures, which depends on analyzing the status of several landmarks of the pelvis, including the obturator ring. With the aim of developing a tool for computer-aided diagnosis and classification of acetabular fractures, this paper addresses the problem of detecting disruptions of the obturator ring. In our study, anteroposterior view x-ray images are used. A simple approach is first developed, then augmented with machine learning algorithms, namely Support Vector Machines and Neural Networks. Results show that an accuracy of 94% could be reached. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.procs.2019.11.078 | Procedia Computer Science |
Keywords | Field | DocType |
acetabular,bone,fracture,machine learning,obturator ring | Data mining,Computer vision,Obturator ring,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Pelvis | Conference |
Volume | ISSN | Citations |
160 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pascal Damien | 1 | 0 | 0.68 |
| Ralph Bou Nader | 2 | 0 | 0.68 |
| Charles Yaacoub | 3 | 0 | 0.34 |
| Jean Lahoud | 4 | 22 | 2.39 |